Congratulations to Huiying on her first peer-review paper!

Recently, Huiying and her coauthors (Dr. Han Wang, Pro. I. Colin Prentice, Pro. Sandy P. Harrison, Dr. Xiangyang Sun and Pro. Genxu Wang) published a new paper on Tree Physiology about predictability of four key photosynthesis-related leaf traits based on optimality-based concepts in Gongga Mountains (Fig. 1). The maximum capacity of carboxylation standardized to 25 ˚C (Vcmax25) is a measure of photosynthesis rate for carbon fixation and the ratio of leaf-internal to ambient CO2 partial pressure (χ) represents the stomatal regulation of CO2 uptake. Leaf nitrogen content (Narea) represents the amount of enzyme responsible for photosynthesis and leaf mass per area (Ma) determines the total carbon cost of leaf construction, which further relates to the period when photosynthesis happens. These key photosynthesis-related traits determine carbon fixation and then influence vegetation production.

There is a large uncertainty in carbon cycle feedback response to climate change, especially for land component. This is partly arising from the model structure and parameters which show large variability in the field (Bonan & Doney, 2018). Understanding physiological processes of plants and climate controls on plant functional traits can help decrease the uncertainty in carbon cycle. Elevation transect provides a convenient and simple way to study trait variability along environmental gradient in a small region.

Huiying and her co-authors tested the predictability of the optimality-based models on those four traits in deciduous species along the Gongga elevation transect. For Vcmax25 and χ, the models developed by Wang et al. (2017) and Smith et al. (2019) were used to estimate them, respectively. The framework in Dong et al. (2017) was adopted here to predict Narea variation. Besides, a newly-developed optimality model built on the hypothesis that plants maximize the leaf life-cycle average net carbon gain was selected to predict LMA variation (Wang et al., 2021).

Fig. 1 Location of 18 sampling sites along an elevational transect. The pictures below show different vegetation types along elevation.

The key advantage of these optimality models is to capture the trait variations with no need of site- or region-specific calibration of parameters. The optimality models can explain nearly 60% of traits variability on average along the elevational transect, in Gongga Mountain using only climate and elevation as inputs (Fig. 2). In addition to their skill in prediction, the optimality models can reveal how the traits response to climate variables. Xu et al. finds that temperature is the most important predictor that drives the variations of Vcmax25 and χ, while radiation for Ma. Narea is proven again to be the sum of metabolic (proportional to Vcmax25) and structural component (proportional to Ma). What’s more, a key implication from optimality model prediction is that Ma adapts to the whole-growing-season environment, but Vcmax25 and χ adapt to environmental conditions during the previous few weeks.

Fig. 2 The comparison of observation vs. prediction. The traits are leaf mass per area (Ma), leaf nitrogen content per unit area (Narea); the maximum capacity of carboxylation standardized to 25 ˚C (Vcmax25) and the ratio of leaf-internal to ambient CO2 partial pressure (χ). Observations are site-mean values. Tg means that traits predicted using mean temperature during the whole growing season, TdJ means that traits predicted using daytime temperature in July.

The successful application of these models provides a new perspective to quantify the separate effects of elevation and climate variables on trait variations and understand the underlying mechanisms. However, the models cannot capture large within-site diversity of photosynthetic traits. Xu et al. demonstrates that hydraulic traits may account for this within-site distribution due to their tight coordination, which requires more attention for model improvement.  

The link of this paper:

Feel free to reach out (Huiying’s email: if you have any questions and want to discuss!


“I cannot thank my supervisor Han, Colin and Sandy enough for revising this manuscript over and over again. Also the field squad members (Colin, Yuechen Chu, Yingying Ji, Meng Li, Xinyu Liu, Giulia Mengoli, Yunke Peng, Shengchao Qiao, Sandy, Yifan Su, Han, Runxi Wang, Yuhui Wu, Shuxia Zhu and Wei Zheng) who collected these amazing data for 2 summers! ” – Huiying Xu


Bonan GB, Doney SC. 2018. Climate, ecosystems, and planetary futures: The challenge to predict life in Earth system models. Science 359(6375).

Dong N, Prentice IC, Evans BJ, Caddy-Retalic S, Lowe AJ, Wright IJ. 2017. Leaf nitrogen from first principles: field evidence for adaptive variation with climate. Biogeosciences 14(2): 481-495.

Smith NG, Keenan TF, Colin Prentice I, Wang H, Wright IJ, Niinemets U, Crous KY, Domingues TF, Guerrieri R, Yoko Ishida F, et al. 2019. Global photosynthetic capacity is optimized to the environment. Ecology Letters 22(3): 506-517.

Wang H, Colin Prentice I, Wright IJ, Qiao S, Xu X, Kikuzawa K, Stenseth NC. 2021. Leaf economics explained by optimality principles. bioRxiv: 2021.2002.2007.430028.

Wang H, Prentice IC, Keenan TF, Davis TW, Wright IJ, Cornwell WK, Evans BJ, Peng C. 2017. Towards a universal model for carbon dioxide uptake by plants. Nature Plants 3(9): 734-741.


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